Xufeng Liang | Searcharxiv

arXiv2026

Ultra-Large-Capacity Passive Quantum Access Network Powered By Single Thermal Source

Quantum Key Distribution (QKD) provides secure keys for classical communications through one-time-pad (OTP) encryption with physical-law security. Advanced PON-based Classical Access Networks (CANs) support up to 256 users with a total rate of 10 Gbps (10-Gbps @ 256-users). The equivalent rate demand of OTP encryption requires QKD Access Networks (QANs) to reach comparable performance, yet state-of-the-art PON-based QANs remain far from this standard. To address this gap, we propose a passive Thermal-State QAN (TS-QAN) distributing polychromatic quantum randomness from a single thermal source and supporting 304 users with an aggregate secret key rate (SKR) of 13 Gbps (13-Gbps @ 304-users). This performance is enabled by three features. First, broadband thermal states with Bose-Einstein statistics can be represented, through the Glauber-Sudarshan representation, as high-bandwidth Gaussian coherent-state ensembles across frequency modes, eliminating many active modulators and quantum random number generators (QRNGs). Second, Electro-Optic (EO) comb beacons provide time-varying polychromatic phase tracking, so each frequency-mode thermal signal can be coherently measured with a Local Local Oscillator (LLO) aided by its beacon, without large-scale phase-locking networks. Third, state broadcasting allows each user to obtain independent final keys via reverse reconciliation after accounting for residual broadcast-induced correlations, expanding network capacity with small SKR losses. Experimentally, we verify a 13-Gbps @ 304-users TS-QAN using Continuous-Variable QKD (CV-QKD) under covariance-matrix-based network security analysis including multimode Holevo leakage and broadcast correlations. This work meets the SKR and capacity demands from CAN to QAN: 13-Gbps @ 304-users satisfies the 10-Gbps @ 256-users benchmark and provides a scalable solution for modern telecommunication systems.

quant-ph↗

arXiv2025

InfoDCL: Informative Noise Enhanced Diffusion Based Contrastive Learning

Contrastive learning has demonstrated promising potential in recommender systems. Existing methods typically construct sparser views by randomly perturbing the original interaction graph, as they have no idea about the authentic user preferences. Owing to the sparse nature of recommendation data, this paradigm can only capture insufficient semantic information. To address the issue, we propose InfoDCL, a novel diffusion-based contrastive learning framework for recommendation. Rather than injecting randomly sampled Gaussian noise, we employ a single-step diffusion process that integrates noise with auxiliary semantic information to generate signals and feed them to the standard diffusion process to generate authentic user preferences as contrastive views. Besides, based on a comprehensive analysis of the mutual influence between generation and preference learning in InfoDCL, we build a collaborative training objective strategy to transform the interference between them into mutual collaboration. Additionally, we employ multiple GCN layers only during inference stage to incorporate higher-order co-occurrence information while maintaining training efficiency. Extensive experiments on five real-world datasets demonstrate that InfoDCL significantly outperforms state-of-the-art methods. Our InfoDCL offers an effective solution for enhancing recommendation performance and suggests a novel paradigm for applying diffusion method in contrastive learning frameworks.

cs.IR↗